Quaternion Exponent Moments and Their Invariants for Color Image

• Autorzy: Yang H.-Y. , Liang L.-L. , Li Y.-W. , Wang X.-Y.

Identyfikatory

DOI

10.3233/FI-2016-1354

• Języki publikacji: EN

Abstrakty

EN

Moments and moment invariants have become a powerful tool in image processing owing to their image description capability and invariance property. But, conventional methods are mainly introduced to deal with the binary or gray-scale images, and the only approaches for color image always have poor color image description capability. Based on Exponent moments (EMs) and quaternion, we introduced the quaternion Exponent moments (QEMs) for describing color images in this paper, which can be seen as the generalization of EMs for gray-level images. It is shown that the QEMs can be obtained from the EMs of each color channel. We derived and analyzed the rotation, scaling, and translation (RST) invariant property of QEMs. We also discussed the problem of color image retrieval using QEMs. Experimental results are provided to illustrate the efficiency of the proposed color image descriptors.

Słowa kluczowe

EN

color image; color image retrieval; exponent moments; geometric invariance; quaternion; quaternion exponent moments

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2016
• Tom: Vol. 145, nr 2
• Strony: 189--205
• Opis fizyczny: Bibliogr. 31 poz., fot., rys., tab., wykr.

Twórcy

autor

Yang H.-Y.

• School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, P.R. China

autor

Liang L.-L.

• School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, P.R. China

autor

Li Y.-W.

• School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, P.R. China

autor

Wang X.-Y.

• School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, P.R. China

Bibliografia

• [1] Yap PT, Paramesran R. Content-based image retrieval using legendre chromaticity distribution moments. IEE Proceedings Vision, Image and Signal Processing. 2006;153(1):17–24. doi:10.1049/ip-vis:20045064 .
• [2] Li S, Lee MC, and Pun CM. Complex zernike moments feature for shape-based image retrieval. IEEE Trans. on Systems, Man and Cybernetics, Part A: Systems and Humans. 2009;39(1):227–237. doi:10.1109/TSMCA.2008.2007988.
• [3] Wang K, Zhang H, Chai L, Hai Y, and Ziliang P. A comparative study of moment-based shape descriptors for product image retrieval. In: 2011 International Conference on Image Analysis and Signal Processing (IASP), Hubei, China. 2011; p.355–359. doi:10.1109/IASP.2011.6109062.
• [4] Papakostas GA, Koulouriotis DE, and Tourassis VD. Performance evaluation of moment-based watermarking methods: A review. Journal of Systems and Software. 2012;85(8):1864–1884. doi:10.1016/j.jss.2012.02.045.
• [5] Hilbert D. Theory of algebraic invariants. Cambridge, U.K.: Cambridge Univ, 1993. ISBN: 9780521449038.
• [6] Schur I. Vorlesungen über invariantentheorie. Berlin, Germany: Springer. 1968. ISBN-10:3642950337.
• [7] Hu MK. Visual pattern recognition by moment invariants. IEEE Trans. on Information Theory. 1962;8(2): 179–187. doi:10.1109/TIT.1962.1057692.
• [8] Revaud J, Lavoue G, and Baskurt A. Improving zernike moments comparison for optimal similarity and rotation angle retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence. 2009;31(4):627–636. doi:10.1109/TPAMI.2008.115.
• [9] Zhu H. Image representation using separable two-dimensional continuous and discrete orthogonal moments. Pattern Recognition. 2012;45(4):1540–1558. doi:10.1016/j.patcog.2011.10.002.
• [10] Bakar SA and Hitam MS. Investigating the properties of zernike moments for robust content based image retrieval. In: 2013 International Conference on Computer Applications Technology (ICCAT). 2013; p.1–6. doi:10.1109/ICCAT.2013.6522011.
• [11] Chen Z, and Sun SK. A zernike moment phase-based descriptor for local image representation and matching. IEEE Trans. on Image Processing. 2010;19(1):205–219. doi:10.1109/TIP.2009.2032890.
• [12] Yap P, Jiang X, and Kot AC. Two-dimensional polar harmonic transforms for invariant image representation. IEEE Trans. on Pattern Analysis and Machine Intelligence. 2010;32(7):1259–1270. Available from: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.119.
• [13] Yang B, and Dai M. Image reconstruction from continuous gaussian chermite moments implemented by discrete algorithm. Pattern Recognition. 2012;45(4):1602–1616. doi:10.1016/j.patcog.2011.10.025.
• [14] Asnaoui K, Aksasse B, and Ouanan M. Content-based color image retrieval based on the 2-d histogram and statistical moments. 2014 Second World Conference on Complex Systems (WCCS). 2014; p.653–656. doi:10.1109/ICoCS.2014.7060982.
• [15] Jiang YJ. Exponent moments and its application in pattern recognition. Beijing: Beijing University of Posts and Telecommunications, 2011.
• [16] Suk T, and Flusser J. Affine moment invariants of color images. In The 13th International Conference on Computer Analysis of Images and Patterns. Mnste, Germany, LNCS, 2009;5702:334–341. doi:10.1007/978-3-642-03767-2 41.
• [17] Abdeslam H, Mhamed S, and Hassan Q. Fast computation of separable two-dimensional discrete invariant moments for image classification. Pattern Recognition, 2015;48(2):509–521. doi:10.1016/j.patcog.2014.08.020.
• [18] Ell TA, and Sangwine SJ. Hypercomplex fourier transforms of color images. IEEE Trans. on Image Processing, 2007;16(1):22–35. doi:10.1109/TIP.2006.884955.
• [19] Dawit A, Lalu M, Kristy FT, and Henning R. Local quaternion Fourier transform and color image texture analysis. Signal Processing, 2010;90(6):1825–1835. doi:10.1016/j.sigpro.2009.11.031.
• [20] Alexiadis DS, and Sergiadis GD. Estimation of motions in color image sequences using hypercomplex Fourier transforms. IEEE Trans. on Image Processing, 2009;18(1):168–197 doi:10.1109/TIP.2008.2007603.
• [21] Chen B, Shu H, and Zhang H. Color image analysis by quaternion zernike moments. In: 20th International Conference on Pattern Recognition (ICPR), 2010;2 p. 625–628. doi:10.1109/ICPR.2010.158.
• [22] Guo L, and Zhu M. Quaternion fouriercmellin moments for color images. Pattern Recognition, 2011;44(2): 187–195. doi:10.1016/j.patcog.2010.08.017.
• [23] Yap PT, Jiang XD, and Kot AC. Two-dimensional polar harmonic transforms for invariant image representation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2010;32(7):1259–1270. doi:10.1109/TPAMI.2009.119.
• [24] Kantor IL, and Solodovnikov AS. Hypercomplex number: an elementary introduction to algebras. Springer-Verlag, NewYork, 1989.
• [25] Pei SC, and Cheng CM. A novel block truncation coding of color images by using quaternion-moment preserving principle. Proceedings of IEEE International Symposium on Circuits and Systems, 1996;2:684–687. doi:10.1109/ISCAS.1996.541817.
• [26] Pei SC, and Cheng CM. A novel block truncation coding of color images by using quaternion-moment preserving principle. IEEE Trans. on Communications, 1997;45(5):583–595. doi:10.1109/26.592558.
• [27] Sangwine SJ, and Ell TA. Hypercomplex auto- and cross- correlation of color images. Proceedings of IEEE International Conference on Image Processing, 1999;4:319–322. doi:10.1109/ICIP.1999.819603.
• [28] Ren H, Ping Z, Bo W, and Wu W. Multidistortion-invariant image recognition with radial harmonic Fourier moments. Journal of the Optical Society of America A, 2003;20(4):631–637. doi:10.1364/JOSAA.20.000631.
• [29] Amayeh G, Erol A, Bebis G, and Nicolescu M. Accurate and efficient computation of high order Zernike moments. In: The First International Symposium on Visual Computing, LNCS, 2005;3804:462–469. doi:10.1007/11595755 56.
• [30] Wang X, Niu P, Yang H, Wang C, and Wang A. A new robust color image watermarking using local quaternion exponent moments. Information Sciences, 2014;277:731–754. doi:10.1016/j.ins.2014.02.158.
• [31] Chen BJ, Shu HZ, and Zhang H. Quaternion zernike moments and their invariants for color image analysis and object recognition. Signal Processing, 2012;92(2):308–318. doi:10.1016/j.sigpro.2011.07.018.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.